Pronostico 2022 - 2026 Maximos Potencia Aparente ET SAN FRANCISCO Fecha: 13/10/2021 12:55

NOTAS: Se dispone de los datos del SITEC ordenados cada 15 minutos por Estacion

1. La demanda en 13 KV del SITEC es la que soportan los trafos de la ET mas la Generacion. Por tanto, los trafos soportan lo que transforma cada uno de 132 a 13 KV.

2. Leer la tabla 3 de Transformadores (Guia de Referencia) y colocar ese pronostico como alternativo

3. Detectar automaticamente los Outliers o Atipicos (LISTO)

INFORME

1. Al informe se lo presento en word o lo presento con nbviewer

2. Si lo presento con word, tengo que pasar Tablaxls a Excel

Seteo Inicial

# -*- coding: utf-8 -*-
"""
Created on Viernes 8 Octubre 2021 17:43:11 

@author: Daniel Busso
"""

# Plantilla de Pre Procesado

# Cómo importar las librerías
import numpy    as np
import pandas   as pd
import datetime as dt
import os

# Gráficos
# ==============================================================================
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
#plt.style.use('fivethirtyeight')

#from statsmodels.graphics.tsaplots import plot_acf
#from statsmodels.graphics.tsaplots import plot_pacf

# Configuración warnings
# ==============================================================================
import warnings
warnings.filterwarnings('ignore') 

# Modelado y Forecasting
# ==============================================================================
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.ensemble import RandomForestRegressor
#from xgboost import XGBRegressor

from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
#from sklearn.metrics import mean_absolute_percentage_error

#from skforecast.ForecasterAutoreg import ForecasterAutoreg
#from skforecast.ForecasterAutoregMultiOutput import ForecasterAutoregMultiOutput
#from skforecast.model_selection import grid_search_forecaster
#from skforecast.model_selection import time_series_spliter
#from skforecast.model_selection import cv_forecaster
#from skforecast.model_selection import backtesting_forecaster
#from skforecast.model_selection import backtesting_forecaster_intervals

# Configuracion Pandas

pd.set_option('display.max_rows', 200)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', -1)
pd.set_option('display.float_format', '{:.2f}'.format)

Lectura de las Demandas en 13 Kv San Francisco

Lectura y creacion del index DateTime para tratar el DataFrame como una Serie de Tiempo

def conversion_2400(fecha):
    if fecha[11:13] != '24':
        return pd.to_datetime(fecha, format='%d/%m/%Y %H:%M')

    fecha = fecha[0:11] + '00:' + fecha[14:]
    return pd.to_datetime(fecha, format='%d/%m/%Y %H:%M') + dt.timedelta(days=1)

# EMPIEZO PROCESANDO UNA CARPETA
# AUTOMATIZAR LECTURAS DE VARIOS ARCHIVOS .PRN ANUALES
# lEER Y CONCATENAR LOS ARCHIVOS .prn DE LA CARPETA DSPractica/eetts/sanfrancisco

import os
import sys
from pathlib import Path

# ELEGIR PLATAFORMA WINDOWS LOCAL O GOOGLE COLABORATORY (la Nube)
if sys.platform == 'linux' :
  from google.colab import drive
  drive.mount('/content/drive', force_remount=True)
  carpeta_trabajo = Path('/content/drive/Othercomputers/LENOVO/eett_prod/san_francisco/Demanda_13') # Google Colab
else:
  carpeta_inicio  = Path.home()
  carpeta_eett = Path("eett_prod/san_francisco/Demanda_13")
  carpeta_trabajo = carpeta_inicio / carpeta_eett

archivos = list(carpeta_trabajo.glob('*.prn'))

# Automatizacion de los DataFrames 
dfconca = pd.DataFrame([])
for nombre_completo in archivos:
    dfdatos = pd.read_fwf(nombre_completo, header=0, decimal=',')
    dfconca = pd.concat([dfconca, dfdatos], ignore_index=True)
    
dfconca    

	Fecha	Hora	MW	MVAR	MVA	Ampere	KVolt
0	01/01/2010	00:15	15.80	7.13	17.33	711.24	0.00
1	01/01/2010	00:30	15.63	6.82	17.05	706.06	0.00
2	01/01/2010	00:45	15.49	6.80	16.91	696.61	0.00
3	01/01/2010	01:00	15.55	6.81	16.98	698.32	0.00
4	01/01/2010	01:15	15.50	6.82	16.93	694.93	0.00
...	...	...	...	...	...	...	...
411454	31/12/2021	23:00	23.25	-2.69	23.41	980.38	4.46
411455	31/12/2021	23:15	22.86	-3.06	23.07	971.29	4.44
411456	31/12/2021	23:30	22.66	-3.20	22.89	972.08	4.43
411457	31/12/2021	23:45	22.77	7.82	24.08	993.98	4.45
411458	31/12/2021	24:00	22.49	7.86	23.82	981.86	4.45

411459 rows × 7 columns

archivos

[WindowsPath('C:/Users/daniel/eett_prod/san_francisco/Demanda_13/MED_DEM_SANFCO13_2010_1201.prn'),
 WindowsPath('C:/Users/daniel/eett_prod/san_francisco/Demanda_13/MED_DEM_SANFCO13_2011_1200.prn'),
 WindowsPath('C:/Users/daniel/eett_prod/san_francisco/Demanda_13/MED_DEM_SANFCO13_2012_1159.prn'),
 WindowsPath('C:/Users/daniel/eett_prod/san_francisco/Demanda_13/MED_DEM_SANFCO13_2013_1157.prn'),
 WindowsPath('C:/Users/daniel/eett_prod/san_francisco/Demanda_13/MED_DEM_SANFCO13_2014_1156.prn'),
 WindowsPath('C:/Users/daniel/eett_prod/san_francisco/Demanda_13/MED_DEM_SANFCO13_2015_1154.prn'),
 WindowsPath('C:/Users/daniel/eett_prod/san_francisco/Demanda_13/MED_DEM_SANFCO13_2016_1152.prn'),
 WindowsPath('C:/Users/daniel/eett_prod/san_francisco/Demanda_13/MED_DEM_SANFCO13_2017_1151.prn'),
 WindowsPath('C:/Users/daniel/eett_prod/san_francisco/Demanda_13/MED_DEM_SANFCO13_2018_1150.prn'),
 WindowsPath('C:/Users/daniel/eett_prod/san_francisco/Demanda_13/MED_DEM_SANFCO13_2019_1150.prn'),
 WindowsPath('C:/Users/daniel/eett_prod/san_francisco/Demanda_13/MED_DEM_SANFCO13_2020_1148.prn'),
 WindowsPath('C:/Users/daniel/eett_prod/san_francisco/Demanda_13/MED_DEM_SANFCO13_2021_1246.prn')]

#
# BORRAMOS COLUMNAS NO USADAS
#

del dfconca['Ampere']
del dfconca['KVolt']

# GENERAMOS INDICE DE FECHAS PARA MANEJAR LA SERIE DE TIEMPO

dfconca['Fecha_Hora'] = dfconca['Fecha'] + ' ' + dfconca['Hora']
dfconca['Fecha_Hora'] = dfconca.Fecha_Hora.apply(conversion_2400) # CONVERSION DE HORA 24:00 ---> 00:00

dfconca.set_index('Fecha_Hora', inplace=True)

serie     = dfconca.drop(dfconca.index[-1]) # borro la fila del nuevo anio
serie     = serie.drop(columns=['Fecha', 'Hora'])

serie_ord = serie.sort_index()

datos = serie_ord.copy()
demandas = datos.copy()
demandas

	MW	MVAR	MVA
Fecha_Hora
2010-01-01 00:15:00	15.80	7.13	17.33
2010-01-01 00:30:00	15.63	6.82	17.05
2010-01-01 00:45:00	15.49	6.80	16.91
2010-01-01 01:00:00	15.55	6.81	16.98
2010-01-01 01:15:00	15.50	6.82	16.93
...	...	...	...
2021-12-31 22:45:00	23.42	-2.79	23.58
2021-12-31 23:00:00	23.25	-2.69	23.41
2021-12-31 23:15:00	22.86	-3.06	23.07
2021-12-31 23:30:00	22.66	-3.20	22.89
2021-12-31 23:45:00	22.77	7.82	24.08

411458 rows × 3 columns

datos

	MW	MVAR	MVA
Fecha_Hora
2010-01-01 00:15:00	15.80	7.13	17.33
2010-01-01 00:30:00	15.63	6.82	17.05
2010-01-01 00:45:00	15.49	6.80	16.91
2010-01-01 01:00:00	15.55	6.81	16.98
2010-01-01 01:15:00	15.50	6.82	16.93
...	...	...	...
2021-12-31 22:45:00	23.42	-2.79	23.58
2021-12-31 23:00:00	23.25	-2.69	23.41
2021-12-31 23:15:00	22.86	-3.06	23.07
2021-12-31 23:30:00	22.66	-3.20	22.89
2021-12-31 23:45:00	22.77	7.82	24.08

411458 rows × 3 columns

Depuracion

Duplicados

if datos.index.has_duplicates:
    print('SI TIENE DUPLICADOS')
else:
    print('NO TIENE DUPLICADOS')

NO TIENE DUPLICADOS

Chequeo Mediciones Faltantes

Si dife distinto de cero, significa que faltan registros/filas en los datos del SITEC

NOTA: Por el momento, no vamos a imputar estos Datos Faltantes ----> NO TIENE SENTIDO POR LA CANTIDAD DE DATOS

#dti2 = pd.date_range(start='2017-01-01 00:15:00', end='2018-01-01 00:00:00', freq="15min")
#
# No se si es tan completo porque a partir del anio 2016 las mediciones van cada 5 min
indice_completo = pd.date_range(start=datos.index[0], end=datos.index[-1], freq="15min")
#
dife = indice_completo.difference(datos.index)

if dife.empty == False:
    print('Faltan algunas Mediciones: ', len(dife), '% Incompletas= ', (len(dife)/len(indice_completo)*100), '% Completas= ', \
          (((len(indice_completo)-len(dife))/len(indice_completo))*100))
else:
    print('\n')
    print('Mediciones Completas: ', (((len(indice_completo)-len(dife))/len(indice_completo))*100))

Faltan algunas Mediciones:  9309 % Incompletas=  2.2123883289326396 % Completas=  97.78761167106737

dife

DatetimeIndex(['2010-08-14 00:00:00', '2010-11-10 00:15:00',
               '2010-11-10 00:30:00', '2010-11-10 00:45:00',
               '2010-11-10 01:00:00', '2010-11-10 01:15:00',
               '2010-11-10 01:30:00', '2010-11-10 01:45:00',
               '2010-11-10 02:00:00', '2010-11-10 02:15:00',
               ...
               '2021-09-17 08:45:00', '2021-09-17 09:00:00',
               '2021-09-17 09:15:00', '2021-09-17 09:30:00',
               '2021-09-18 08:45:00', '2021-09-18 09:00:00',
               '2021-09-18 09:15:00', '2021-09-18 09:30:00',
               '2021-12-03 09:00:00', '2021-12-03 09:15:00'],
              dtype='datetime64[ns]', length=9309, freq=None)

#
# OBTENGO LAS MEDICIONES FALTANTES FECHAS-HORA SIN MEDICION

dsm = pd.DataFrame({'Mediciones Faltantes':dife}, index=dife)
dsm

	Mediciones Faltantes
2010-08-14 00:00:00	2010-08-14 00:00:00
2010-11-10 00:15:00	2010-11-10 00:15:00
2010-11-10 00:30:00	2010-11-10 00:30:00
2010-11-10 00:45:00	2010-11-10 00:45:00
2010-11-10 01:00:00	2010-11-10 01:00:00
...	...
2021-09-18 09:00:00	2021-09-18 09:00:00
2021-09-18 09:15:00	2021-09-18 09:15:00
2021-09-18 09:30:00	2021-09-18 09:30:00
2021-12-03 09:00:00	2021-12-03 09:00:00
2021-12-03 09:15:00	2021-12-03 09:15:00

9309 rows × 1 columns

# AGRUPO LAS MEDICIONES FALTANTES
#

dsm['anio'] = dsm.index.year
dsm['mes']  = dsm.index.month
dsm['dia']  = dsm.index.day
dsm_grp = dsm.groupby(by=['anio','mes', 'dia'])

med_faltantes = dsm_grp.count()
med_faltantes

# MEDICIONES FALTANTES PARA UN ANIO EN PARTICULAR
#med_faltantes.loc[(2010), :]

# MEDICIONES FALTANTES PARA UN DIA EN PARTICULAR
#
#med_faltantes.loc[(2010,11,10), :]


#dsm_grp.get_group((2010, 11, 10))
#dsm_grp.groups
#dsm_grp.indices

			Mediciones Faltantes
anio	mes	dia
2010	8	14	1
	11	10	95
		11	1
2011	2	3	1
2012	1	12	95
...	...	...	...
2021	8	13	5
		27	1
	9	17	6
		18	4
	12	3	2

278 rows × 1 columns

dsm

	Mediciones Faltantes	anio	mes	dia
2010-08-14 00:00:00	2010-08-14 00:00:00	2010	8	14
2010-11-10 00:15:00	2010-11-10 00:15:00	2010	11	10
2010-11-10 00:30:00	2010-11-10 00:30:00	2010	11	10
2010-11-10 00:45:00	2010-11-10 00:45:00	2010	11	10
2010-11-10 01:00:00	2010-11-10 01:00:00	2010	11	10
...	...	...	...	...
2021-09-18 09:00:00	2021-09-18 09:00:00	2021	9	18
2021-09-18 09:15:00	2021-09-18 09:15:00	2021	9	18
2021-09-18 09:30:00	2021-09-18 09:30:00	2021	9	18
2021-12-03 09:00:00	2021-12-03 09:00:00	2021	12	3
2021-12-03 09:15:00	2021-12-03 09:15:00	2021	12	3

9309 rows × 4 columns

med_faltantes.loc[(2014), :]

		Mediciones Faltantes
mes	dia
3	19	6
5	16	1
6	19	95
	20	96
	21	96
	22	96
	23	1

Analizar y Encontrar Errores de Medicion usando graficas

Dejar para mas adelante la deteccion automatica de Errores de Medicion

# Gráfico boxplot para estacionalidad anual 
# Cada Box Plot grafica el mes para los 12 anios 
# Por ejemplo Octubre '10' son todos los valores de Octubre en los 12 anios
# ==============================================================================
fig, ax = plt.subplots(figsize=(10, 5))
plt.style.use('fivethirtyeight')

datos['mes'] = datos.index.month
datos.boxplot(column='MVA', by='mes', ax=ax)
datos.groupby('mes')['MVA'].median().plot(style='o-', linewidth=0.8, ax=ax)

# ESTETICA DEL GRAFICO
plt.xticks(fontsize=12)  #plt.xticks(fontsize=15, rotation=90)
plt.yticks(fontsize=12)
#ax.set_ylabel('Demandas')
ax.set_title('Distribución Demandas por mes 2010 - 2021')
ax.legend()
fig.suptitle('');

# Gráfico boxplot para estacionalidad anual 
# Blox Plot para todos los anios
# ==============================================================================

def Grafica_Caja(pi, pf):
    
    plt.style.use('fivethirtyeight')
    fig = plt.figure()
    fig, ax = plt.subplots(figsize=(30, 5), dpi=100)  # (15, 5)
    
    # PREPARO LOS DATOS
    datos['mes']  = datos.index.month
    datos['anio'] = datos.index.year
    datos2 = datos[pi:pf]
    
    # GRAFICO EL BOXPLOT
    datos2.boxplot(column='MVA', by=['anio','mes'], ax=ax)
    
    # GRAFICO LA LINEA DE MEDIANA
    y = datos2.groupby(by=['anio','mes'])['MVA'].median()
    pos_sup = y.index.shape[0] + 1
    x = np.arange(1, pos_sup)
    ax.plot(x, y, lw=1.5, label='Mediana [MVA]', marker='o')
    #datos3.groupby(by=['anio','mes'])['MVA'].median().plot(style='o-', linewidth=0.8, ax=ax)
    
    # ESTETICA DEL GRAFICO
    plt.xticks(fontsize=12, rotation=90)  #plt.xticks(fontsize=15, rotation=90)
    plt.yticks(fontsize=12)
    #ax.set_ylabel('Demandas')
    ax.set_title('Distribución Demandas por Mes ' +pi+' - '+pf)
    ax.legend() 
    fig.suptitle('');

Grafica_Caja('2010', '2021')

<Figure size 432x288 with 0 Axes>

Grafica_Caja('2017', '2017')

<Figure size 432x288 with 0 Axes>

Grafica_Caja('2019', '2019')

<Figure size 432x288 with 0 Axes>

Grafica_Caja('2020', '2020')

<Figure size 432x288 with 0 Axes>

'### Remover Datos Atipicos'

# CALCULO FORMAL DE LOS LIMITES SUPERIOR E INFERIOR
# Demanda = Transformacion 132/13 Kv + Generacion Propia
# Demanda = TD1 + TD2 + TD3 + TG1 + TG2

#Pot_Ins_Tra = pd.Series([25.0, 25.0, 25.0], index=['TD1', 'TD2', 'TD3'])
#POT_INS_TRAFOS = Pot_Ins_Tra.sum()

#Pot_Ins_Gen = pd.Series([20.0, 28.0], index=['TG1', 'TG2'])  # con cos fi = 0.8 TG1 = 16 MW y TG2 = 23 MW 
#POT_INS_GENE = Pot_Ins_Gen.sum()
#POT_TOTAL = POT_INS_TRAFOS + POT_INS_GENE


#indice_ini = datos.index[0] 
#delta = pd.Timedelta(value=90, unit='D')
#indice_fin = indice_ini + delta
#verano = datos.loc[indice_ini:indice_fin, 'MVA'] 

#===============================================================================================================
# LA IDEA ES CALCULAR LOS LIMITES PARA PERIODOS ESTIVALES DE 6 MESES
# PERIODO VERANO1  '2020-01-01':'2020-03-31'
# PERIODO INVIERNO '2020-04-01:2020-09-30'
# PERIODO VERANO2  '2020-10-01:2020-12-31'
#
def lim_sup(q75, q25):
    IQR = q75 - q25
    bigote = 3 * IQR       # LO NORMAL SE USA 1.5
    return (q75 + bigote)
    
atipicos = pd.Series([])    
anios = ['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021']
for anio in anios:
    ver1i = anio + '-01-01 00' 
    ver1f = anio + '-03-31 23'
    invi  = anio + '-04-01 00' 
    invf  = anio + '-09-30 23'
    ver2i = anio + '-10-01 00' 
    ver2f = anio + '-12-31 23'
    
    #verano1  = demandas.loc[ver1i:ver1f, 'MVA'].resample('D').max()
    #invierno = demandas.loc[invi:invf, 'MVA'].resample('D').max()
    #verano2  = demandas.loc[ver2i:ver2f, 'MVA'].resample('D').max()
    verano1  = demandas.loc[ver1i:ver1f, 'MVA']
    invierno = demandas.loc[invi:invf, 'MVA']
    verano2  = demandas.loc[ver2i:ver2f, 'MVA']
    
    lim_sup_ver1 = lim_sup(verano1.quantile(0.75), verano1.quantile(0.25)) 
    lim_sup_inv  = lim_sup(invierno.quantile(0.75), invierno.quantile(0.25)) 
    lim_sup_ver2 = lim_sup(verano2.quantile(0.75), verano2.quantile(0.25)) 
    
    # REMOVER EJEMPLOS DE ATIPICOS 
    #indice1 = pd.to_datetime('2010-01-01 19:08:08')
    #indice  = pd.to_datetime('2010-04-01 19:12:12')
    #indice2 = pd.to_datetime('2010-10-01 19:14:14')
    #demandas.loc[indice1, 'MVA'] = 150.0
    #demandas.loc[indice, 'MVA'] = 150.0
    #demandas.loc[indice2, 'MVA'] = 150.0
    
    # GUARDAR LOS ATIPICOS A REMOVER Y LOS VALORES
    atipicos_ver1 = verano1[verano1   > lim_sup_ver1] 
    atipicos_inv  = invierno[invierno > lim_sup_inv]
    atipicos_ver2 = verano2[verano2   > lim_sup_ver2]    
    atipicos = pd.concat([atipicos, atipicos_ver1, atipicos_inv, atipicos_ver2]) 

atipicos_ord = atipicos.sort_index()
#datos = demandas.drop(atipicos.index)
atipicos_ord

2017-01-27 09:30:00   75.78
2017-04-22 08:15:00   28.43
2017-04-22 08:30:00   28.23
2017-04-22 08:45:00   42.52
2017-04-22 09:00:00   41.99
                       ... 
2020-08-22 11:45:00   22.96
2020-08-22 12:15:00   22.92
2020-08-22 13:30:00   23.74
2020-08-22 13:45:00   23.29
2021-05-04 11:30:00   59.22
Length: 476, dtype: float64

#atipicos_ord['2017-01'].head(100)

Deteccion de Valores Erroneos de Medicion

Valores constantes

#
# Datos
#
# Tengo que usar pandas. rowling

Valores Picos Erroneos

Normalidad de los datos y Seleccion de la Distribucion

Borrar Mediciones Erroneas

#
# BORRO DATOS ATIPICOS FALSOS 

#foto = datos.loc['2017-01-27 09:30:00':'2017-01-27 09:30:00'].index
#datos = datos.drop(foto)

# BORRAMOS ANIO 01/2017
indice = datos.loc['2017-01', 'MVA'].idxmax()
datos = datos.drop(indice)

#
# BORRAMOS DATOS ANIO 10-11-12/2020
#
desde = '2020-10-06 17:15:00'
hasta = '2020-12-14 14:45:00'
indice = datos.loc[desde:hasta].index
datos  = datos.drop(indice)


desde = pd.to_datetime(desde)
hasta = pd.to_datetime(hasta)
duracion = hasta - desde
print('Se borro: ', duracion, 'que equivale a tantas mediciones tomadas de a 15 min') 

# BORRAMOS ANIO 05/2021
indice = datos.loc['2021-05', 'MVA'].idxmax()
datos = datos.drop(indice)

Se borro:  68 days 21:30:00 que equivale a tantas mediciones tomadas de a 15 min

Graficas Anuales

Box Plot Mensual del total de Datos

# Gráfico boxplot para estacionalidad anual 
# Cada Box Plot grafica el mes para los 12 anios 
# Por ejemplo Octubre '10' son todos los valores de Octubre en los 12 anios
# ==============================================================================
fig, ax = plt.subplots(figsize=(10, 5))
plt.style.use('fivethirtyeight')

datos['mes'] = datos.index.month
datos.boxplot(column='MVA', by='mes', ax=ax)
datos.groupby('mes')['MVA'].median().plot(style='o-', linewidth=0.8, ax=ax)

# ESTETICA DEL GRAFICO
plt.xticks(fontsize=12)  #plt.xticks(fontsize=15, rotation=90)
plt.yticks(fontsize=12)
#ax.set_ylabel('Demandas')
ax.set_title('Distribución Demandas por mes 2010 - 2021')
ax.legend()
fig.suptitle('');

Blox Plot Mensual

# Gráfico boxplot para estacionalidad anual 
# Blox Plot para todos los anios
# ==============================================================================

def Grafica_Caja(pi, pf):
    
    plt.style.use('fivethirtyeight')
    fig = plt.figure()
    fig, ax = plt.subplots(figsize=(30, 5), dpi=100)  # (15, 5)
    
    # PREPARO LOS DATOS
    datos['mes']  = datos.index.month
    datos['anio'] = datos.index.year
    datos2 = datos[pi:pf]
    
    # GRAFICO EL BOXPLOT
    datos2.boxplot(column='MVA', by=['anio','mes'], ax=ax)
    
    # GRAFICO LA LINEA DE MEDIANA
    y = datos2.groupby(by=['anio','mes'])['MVA'].median()
    pos_sup = y.index.shape[0] + 1
    x = np.arange(1, pos_sup)
    ax.plot(x, y, lw=1.5, label='Mediana [MVA]', marker='o')
    #datos3.groupby(by=['anio','mes'])['MVA'].median().plot(style='o-', linewidth=0.8, ax=ax)
    
    # ESTETICA DEL GRAFICO
    plt.xticks(fontsize=12, rotation=90)  #plt.xticks(fontsize=15, rotation=90)
    plt.yticks(fontsize=12)
    #ax.set_ylabel('Demandas')
    ax.set_title('Distribución Demandas por Mes ' +pi+' - '+pf)
    ax.legend() 
    fig.suptitle('');

Grafica Maximos Semanales

# GRAFICA MAXIMOS SEMANALES

def Grafica_xSemana(pi, pf):
    z = datos.loc[pi:pf, 'MVA'].resample('W').max() # Datos
    x = z.index # Tiempo

    plt.style.use('fivethirtyeight')
    fig = plt.figure()
    fig, grafico = plt.subplots(figsize=(30, 5), dpi=100)
    
    grafico.plot(x, z, lw=1, label='Potencia Aparente', marker='o')
    grafico.set_xlabel('[ Meses ]')  # Add an x-label to the axes.
    grafico.set_ylabel('[ MVA ]')  # Add a y-label to the axes.
    grafico.set_title("POTENCIA APARENTE SEMANAL " + pi + ' ' + pf)  # Add a title to the axes.
    #grafico.legend()  # Add a legend.
    #grafico.annotate('Pico Max', xy=(01-31 14:45, 75), xytext=(01-31 15:00, 75), arrowprops=dict(facecolor='black', shrink=0.05)
    #grafico.grid()
    #fig.savefig("Todos_Anios_xSemana.png")

Grafica Por Minuto (cada 15 Minutos)

# Grafica anual de la Potencia Aparente cada 15 minutos

def Grafica_xmin(pi, pf):
    y = datos.loc[pi:pf, 'MVA']
    x = y.index
    
    plt.style.use('fivethirtyeight')
    fig = plt.figure()
    fig, grafico = plt.subplots(figsize=(30, 5), dpi=100)
    
    grafico.plot(x, y, lw=1.5, marker='o', label='Potencia Aparente x Minuto')
    # grafico.plot(x, y, lw=1, label='Potencia Aparente', marker='o')
    grafico.set_xlabel('[ Meses ]')  # Add an x-label to the axes.
    grafico.set_ylabel('[ MVA ]')  # Add a y-label to the axes.
    grafico.set_title("POTENCIA APARENTE [cada 15 minutos] " + anio)  # Add a title to the axes.
    #grafico.legend(loc='lower left')  # Add a legend.
    
    #grafico.grid(visible='True', which='major', axis='both', color='black', alpha=1, linewidth=0.5)
    grafico.grid(visible='True', which='major', axis='both', color='black', linewidth=0.5)
    grafico.grid(visible='True', which='minor', axis='both', color='black', linewidth=0.5)
    
    x2 = pd.date_range(start=pi+'-01-01', end=pf+'-12-31', periods=5)
    grafico.set_xticks(x2)
    #grafico.annotate('Pico Max', xy=(01-31 14:45, 75), xytext=(01-31 15:00, 75), arrowprops=dict(facecolor='black', shrink=0.05)
    #grafico.grid()
    #fig.savefig("Serie_Anual_15min_"+anio+".png")

Grafica por Dia

# Grafica del Pico Maximo Potencia Aparente x DIA
from matplotlib import ticker

def Grafica_xDia(pi, pf):
    y = datos.loc[pi:pf, 'MVA'].resample('D').max()
    x = y.index
    
    plt.style.use('fivethirtyeight')
    fig = plt.figure()
    fig, grafico = plt.subplots(figsize=(30, 5), dpi=100)
    
    grafico.plot(x, y, lw=1.5, label='Potencia Aparente x Dia', marker='o')
    grafico.set_xlabel('[ Meses ]')  # Add an x-label to the axes.
    grafico.set_ylabel('[ MVA ]')  # Add a y-label to the axes.
    grafico.set_title("POTENCIA APARENTE DIARIA " + anio)  # Add a title to the axes.
    #grafico.legend(loc='lower left')  # Add a legend.
    
    #grafico.grid(visible='True', which='major', axis='both', color='black', alpha=1, linewidth=0.5)
    grafico.grid(visible='True', which='major', axis='both', color='black', linewidth=0.5)
    grafico.grid(visible='True', which='minor', axis='both', color='black', linewidth=0.5)
    
    x2 = pd.date_range(start=pi+'-01-01', end=pf+'-12-31', periods=5)
    grafico.set_xticks(x2)
    
    #grafico.annotate('Pico Max', xy=(01-31 14:45, 75), xytext=(01-31 15:00, 75), arrowprops=dict(facecolor='black', shrink=0.05)
    #grafico.grid()
    #fig.savefig("Serie_Anual_xDia_"+anio+".png")         

Grafica del Pico Maximo Anual

# GRAFICA DEL PICO MAXIMO POTENCIA APARENTE (CADA 15 MINUTOS) VENTANA 12 hs ANTES y 12 hs DESPUES
#
def Grafica_Pico_Anual(pi, pf):
    indice_max = datos.loc[pi:pf, 'MVA'].idxmax()
    delta = pd.Timedelta('12 hours')
    indice_mas12   = indice_max + delta
    indice_menos12 = indice_max - delta
    maximo = datos.loc[indice_menos12:indice_mas12, 'MVA'].max()
    
    y = datos.loc[indice_menos12 : indice_mas12, 'MVA'] # Datos
    x = y.index  # Tiempo
    
    plt.style.use('fivethirtyeight')
    fig = plt.figure()
    fig, ax = plt.subplots(figsize=(10, 3), dpi=100) #fig, ax = plt.subplots(figsize=(10, 5), dpi=100)
    #Rotulo = 'Pico: ' + str(indice_max) + ' ' + str(maximo)
    
    ax.plot(x, y, lw=1, marker='o') #ax.plot(x, y, lw=1, label=Rotulo, marker='o')
    ax.minorticks_on()
    #ax.grid(b=True, which='major', color='blue', alpha=1, linewidth=1)
    #ax.grid(b=True, which='minor', color='beige', alpha=1, linewidth=1)
    # Major Tick Label
    # Minor Tick Label
    plt.xticks(fontsize=10, rotation=90)
    plt.yticks(fontsize=10)
    ax.set_xlabel('[ Horas ]', fontsize=10)  # Add an x-label to the axes.
    ax.set_ylabel('[ MVA ]', fontsize=10)  # Add a y-label to the axes.
    ax.set_title("PICO MAXIMO POTENCIA APARENTE " + str(maximo) + ' ' + str(indice_max), fontsize=12)  # Add a title to the axes.
    #ax.legend(loc='lower left', fontsize=10)  # Add a legend.
    #fig.savefig("Pico_Anual_"+anio+".png")
        
    
    #print(indice_menos12)
    print(indice_max, datos.loc[indice_menos12:indice_mas12, 'MW'].max(), datos.loc[indice_menos12:indice_mas12, 'MVA'].max())
    #print(indice_mas12)
    #maximo = datos.loc[indice_max:indice_max, 'MVA']
    #print(maximo[0])

Grafica_xmin('2014-06-18 12', '2014-06-23 16') 

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-41-a46526f9db4c> in <module>
----> 1 Grafica_xmin('2014-06-18 12', '2014-06-23 16')

<ipython-input-40-57faf5f979cf> in Grafica_xmin(pi, pf)
     13     grafico.set_xlabel('[ Meses ]')  # Add an x-label to the axes.
     14     grafico.set_ylabel('[ MVA ]')  # Add a y-label to the axes.
---> 15     grafico.set_title("POTENCIA APARENTE [cada 15 minutos] " + anio)  # Add a title to the axes.
     16     #grafico.legend(loc='lower left')  # Add a legend.
     17 

NameError: name 'anio' is not defined

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Grafica_xmin('2014-06-18', '2014-06-18') 

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-42-1db7e8722eea> in <module>
----> 1 Grafica_xmin('2014-06-18', '2014-06-18')

<ipython-input-40-57faf5f979cf> in Grafica_xmin(pi, pf)
     13     grafico.set_xlabel('[ Meses ]')  # Add an x-label to the axes.
     14     grafico.set_ylabel('[ MVA ]')  # Add a y-label to the axes.
---> 15     grafico.set_title("POTENCIA APARENTE [cada 15 minutos] " + anio)  # Add a title to the axes.
     16     #grafico.legend(loc='lower left')  # Add a legend.
     17 

NameError: name 'anio' is not defined

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# LANZO LAS GRAFICAS 

anios = ['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021']

Grafica_xSemana(anios[0], anios[-1])
Grafica_Caja(anios[0], anios[-1])

for anio in anios:
    Grafica_xmin(anio, anio)
    Grafica_Caja(anio, anio)
    Grafica_xDia(anio, anio)
    Grafica_Pico_Anual(anio, anio)

2010-01-05 14:00:00 36.24 37.32
2011-12-20 13:30:00 39.13 40.2
2012-02-16 14:00:00 42.13 43.18
2013-12-26 15:00:00 41.97 42.7
2014-01-23 14:00:00 48.77 49.73
2015-03-11 14:00:00 44.64 46.34
2016-02-12 13:15:00 45.98 46.79
2017-03-02 13:15:00 45.16 45.48
2018-02-07 13:45:00 46.22 46.35
2019-02-22 14:15:00 45.63 45.77
2020-12-28 13:15:00 41.71 41.74
2021-12-29 14:15:00 48.65 48.67

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Pronosticos

Tabla Historica

# 
# GENERAR TABLA EXCEL CON LOS DATOS DEPURADOS
#
anios_hist = ['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021']

Trafos = pd.Series([25.0, 25.0, 25.0], index=['TD1', 'TD2', 'TD3'])
POT_INSTALADA = Trafos.sum()

Historica = pd.DataFrame(columns = ['FECHA_HORA', 'P_MW', 'Q_MVAR', 'S_MVA'])

for anio in anios_hist:
    #Anio = pd.to_numeric(anio)
    fecha_hora_i = datos.loc[anio:anio, 'MVA'].idxmax()
    maximo_p     = datos.loc[fecha_hora_i, 'MW'].max()
    maximo_q     = datos.loc[fecha_hora_i, 'MVAR'].max()
    maximo_s     = datos.loc[fecha_hora_i, 'MVA'].max()
    Historica.loc[anio] = [fecha_hora_i, maximo_p, maximo_q, maximo_s]  
    
# Columnas derivadas
Historica['POT_INSTALADA'] = POT_INSTALADA
Historica['CRECIMIENTO_%'] = Historica['S_MVA'].pct_change().mul(100)
Historica['N-1'] = Historica['POT_INSTALADA'] - Trafos.max()   
Historica['CARGA_%'] = Historica['S_MVA'] / Historica['POT_INSTALADA'] * 100
Historica['ANIO'] = pd.to_numeric(Historica.index)

nombres_columnas = ['ANIO', 'FECHA_HORA', 'P_MW', 'Q_MVAR', 'S_MVA', 'CRECIMIENTO_%', 'POT_INSTALADA', 'N-1', 'CARGA_%']
Historica = Historica.reindex(columns=nombres_columnas)
Historica

	ANIO	FECHA_HORA	P_MW	Q_MVAR	S_MVA	CRECIMIENTO_%	POT_INSTALADA	N-1	CARGA_%
2010	2010	2010-01-05 14:00:00	36.24	8.89	37.32	NaN	75.00	50.00	49.76
2011	2011	2011-12-20 13:30:00	39.13	9.23	40.20	7.72	75.00	50.00	53.60
2012	2012	2012-02-16 14:00:00	42.13	9.47	43.18	7.41	75.00	50.00	57.57
2013	2013	2013-12-26 15:00:00	41.97	7.88	42.70	-1.11	75.00	50.00	56.93
2014	2014	2014-01-23 14:00:00	48.67	10.21	49.73	16.46	75.00	50.00	66.31
2015	2015	2015-03-11 14:00:00	44.64	12.43	46.34	-6.82	75.00	50.00	61.79
2016	2016	2016-02-12 13:15:00	45.93	8.94	46.79	0.97	75.00	50.00	62.39
2017	2017	2017-03-02 13:15:00	45.16	5.41	45.48	-2.80	75.00	50.00	60.64
2018	2018	2018-02-07 13:45:00	46.22	3.40	46.35	1.91	75.00	50.00	61.80
2019	2019	2019-02-22 14:15:00	45.63	3.57	45.77	-1.25	75.00	50.00	61.03
2020	2020	2020-12-28 13:15:00	41.71	1.59	41.74	-8.80	75.00	50.00	55.65
2021	2021	2021-12-29 14:15:00	48.65	1.51	48.67	16.60	75.00	50.00	64.89

Historica.index

Index(['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018',
       '2019', '2020', '2021'],
      dtype='object')

Regresion Lineal

pd.set_option('display.float_format', '{:.8f}'.format)
import statsmodels.formula.api as smf
lm = smf.ols(formula="S_MVA~ANIO", data = Historica).fit()
lm.summary2()

Model:	OLS	Adj. R-squared:	0.259
Dependent Variable:	S_MVA	AIC:	62.9670
Date:	2022-06-14 13:43	BIC:	63.9368
No. Observations:	12	Log-Likelihood:	-29.483
Df Model:	1	F-statistic:	4.842
Df Residuals:	10	Prob (F-statistic):	0.0524
R-squared:	0.326	Scale:	9.5674

	Coef.	Std.Err.	t	P>|t|	[0.025	0.975]
Intercept	-1102.6916	521.3294	-2.1152	0.0605	-2264.2860	58.9027
ANIO	0.5692	0.2587	2.2006	0.0524	-0.0071	1.1455

Omnibus:	1.100	Durbin-Watson:	1.424
Prob(Omnibus):	0.577	Jarque-Bera (JB):	0.040
Skew:	0.056	Prob(JB):	0.980
Kurtosis:	3.260	Condition No.:	1176765

pd.set_option('display.float_format', '{:.2f}'.format)

Historica['S_MVA_PRED'] = lm.predict(Historica['ANIO'])

nombres_columnas = ['ANIO', 'FECHA_HORA', 'P_MW', 'Q_MVAR', 'S_MVA', 'CRECIMIENTO_%', 'S_MVA_PRED', 'POT_INSTALADA', 'N-1', 'CARGA_%']
Historica = Historica.reindex(columns=nombres_columnas)
Historica

	ANIO	FECHA_HORA	P_MW	Q_MVAR	S_MVA	CRECIMIENTO_%	S_MVA_PRED	POT_INSTALADA	N-1	CARGA_%
2010	2010	2010-01-05 14:00:00	36.24	8.89	37.32	NaN	41.39	75.00	50.00	49.76
2011	2011	2011-12-20 13:30:00	39.13	9.23	40.20	7.72	41.96	75.00	50.00	53.60
2012	2012	2012-02-16 14:00:00	42.13	9.47	43.18	7.41	42.53	75.00	50.00	57.57
2013	2013	2013-12-26 15:00:00	41.97	7.88	42.70	-1.11	43.10	75.00	50.00	56.93
2014	2014	2014-01-23 14:00:00	48.67	10.21	49.73	16.46	43.67	75.00	50.00	66.31
2015	2015	2015-03-11 14:00:00	44.64	12.43	46.34	-6.82	44.24	75.00	50.00	61.79
2016	2016	2016-02-12 13:15:00	45.93	8.94	46.79	0.97	44.81	75.00	50.00	62.39
2017	2017	2017-03-02 13:15:00	45.16	5.41	45.48	-2.80	45.38	75.00	50.00	60.64
2018	2018	2018-02-07 13:45:00	46.22	3.40	46.35	1.91	45.95	75.00	50.00	61.80
2019	2019	2019-02-22 14:15:00	45.63	3.57	45.77	-1.25	46.51	75.00	50.00	61.03
2020	2020	2020-12-28 13:15:00	41.71	1.59	41.74	-8.80	47.08	75.00	50.00	55.65
2021	2021	2021-12-29 14:15:00	48.65	1.51	48.67	16.60	47.65	75.00	50.00	64.89

print('Parametros : ', lm.params) 
print('R2 : ', lm.rsquared)

Parametros :  Intercept   -1102.69
ANIO        0.57    
dtype: float64
R2 :  0.3262571112710426

Tabla de Pronosticos

#
#  AGREGO LOS AÑOS DE PRONOSTICO PROPIAMENTE DICHO
#
#Pronostico  = Tablaxls.copy()
pd.set_option('display.float_format', '{:.2f}'.format)
anios_pro = ['2022', '2023', '2024', '2025', '2026']
POT_INSTALADA = Trafos.sum()
Precaucion    = POT_INSTALADA - Trafos.max()

Anio = pd.Series(pd.to_numeric(anios_pro), index=anios_pro)
Fecha_Hora = pd.Series(np.nan, index= anios_pro)
P_MW   = pd.Series(np.nan, index=anios_pro)
Q_MVAR = pd.Series(np.nan, index=anios_pro)
S_MVA  = pd.Series(np.nan, index=anios_pro)
Crecimiento = pd.Series(np.nan, index=anios_pro)

Pronostico = pd.DataFrame({'ANIO':Anio, 
                           'FECHA_HORA':Fecha_Hora, 
                           'P_MW':P_MW, 
                           'Q_MVAR':Q_MVAR,
                           'S_MVA':S_MVA,
                           'CRECIMIENTO_%':Crecimiento}, index=anios_pro)

# El MODELO DE REGRESION LINEAL          
Pronostico['S_MVA_PRED'] = lm.predict(Pronostico['ANIO'])  # Pronostico['S_MVA_PRED'] = 0.51372727*Pronostico["ANIO"] - 991.015000
#
Pronostico['POT_INSTALADA'] = POT_INSTALADA
Pronostico['N-1'] = Pronostico['POT_INSTALADA'] - Trafos.max()  
Pronostico['CARGA_%'] = Pronostico['S_MVA_PRED'] / Pronostico['POT_INSTALADA'] * 100 

# nombres_columnas = ['FECHA_HORA', 'P_MW', 'Q_MVAR', 'S_MVA', 'Crecimiento %', 'Pot_Instalada', 'N-1', '% Carga']
#Tablaxls = Tablaxls.reindex(columns=nombres_columnas)

Pronostico

	ANIO	FECHA_HORA	P_MW	Q_MVAR	S_MVA	CRECIMIENTO_%	S_MVA_PRED	POT_INSTALADA	N-1	CARGA_%
2022	2022	NaN	NaN	NaN	NaN	NaN	48.22	75.00	50.00	64.30
2023	2023	NaN	NaN	NaN	NaN	NaN	48.79	75.00	50.00	65.06
2024	2024	NaN	NaN	NaN	NaN	NaN	49.36	75.00	50.00	65.81
2025	2025	NaN	NaN	NaN	NaN	NaN	49.93	75.00	50.00	66.57
2026	2026	NaN	NaN	NaN	NaN	NaN	50.50	75.00	50.00	67.33

#
# CONCATENAR LA TABLA HISTORICA + TABLA PRONOSTICO
#
Tablaxls = pd.concat([Historica,Pronostico], axis = 0)
Tablaxls

	ANIO	FECHA_HORA	P_MW	Q_MVAR	S_MVA	CRECIMIENTO_%	S_MVA_PRED	POT_INSTALADA	N-1	CARGA_%
2010	2010	2010-01-05 14:00:00	36.24	8.89	37.32	NaN	41.39	75.00	50.00	49.76
2011	2011	2011-12-20 13:30:00	39.13	9.23	40.20	7.72	41.96	75.00	50.00	53.60
2012	2012	2012-02-16 14:00:00	42.13	9.47	43.18	7.41	42.53	75.00	50.00	57.57
2013	2013	2013-12-26 15:00:00	41.97	7.88	42.70	-1.11	43.10	75.00	50.00	56.93
2014	2014	2014-01-23 14:00:00	48.67	10.21	49.73	16.46	43.67	75.00	50.00	66.31
2015	2015	2015-03-11 14:00:00	44.64	12.43	46.34	-6.82	44.24	75.00	50.00	61.79
2016	2016	2016-02-12 13:15:00	45.93	8.94	46.79	0.97	44.81	75.00	50.00	62.39
2017	2017	2017-03-02 13:15:00	45.16	5.41	45.48	-2.80	45.38	75.00	50.00	60.64
2018	2018	2018-02-07 13:45:00	46.22	3.40	46.35	1.91	45.95	75.00	50.00	61.80
2019	2019	2019-02-22 14:15:00	45.63	3.57	45.77	-1.25	46.51	75.00	50.00	61.03
2020	2020	2020-12-28 13:15:00	41.71	1.59	41.74	-8.80	47.08	75.00	50.00	55.65
2021	2021	2021-12-29 14:15:00	48.65	1.51	48.67	16.60	47.65	75.00	50.00	64.89
2022	2022	NaN	NaN	NaN	NaN	NaN	48.22	75.00	50.00	64.30
2023	2023	NaN	NaN	NaN	NaN	NaN	48.79	75.00	50.00	65.06
2024	2024	NaN	NaN	NaN	NaN	NaN	49.36	75.00	50.00	65.81
2025	2025	NaN	NaN	NaN	NaN	NaN	49.93	75.00	50.00	66.57
2026	2026	NaN	NaN	NaN	NaN	NaN	50.50	75.00	50.00	67.33

Graficas al 2026

Ver de hacer con seaborn

# Grafica anual de la Potencia Aparente cada 15 minutos
# (Trafos + Generacion)

def Grafica_Hist_Pred():
    plt.style.use('fivethirtyeight')
    
    #y = Tablaxls.loc['2010':'2021', 'S_MVA']
    #x_y = Tablaxls.loc['2010':'2021', 'ANIO']
    
    y = Tablaxls.loc[:, 'S_MVA']
    pred = Tablaxls.loc[:, 'S_MVA_PRED']
    p_inst = Tablaxls.loc[:, 'POT_INSTALADA']
    precau = Tablaxls.loc[:, 'N-1']
    
    x = Tablaxls.loc[:, 'ANIO']
    
    fig = plt.figure()
    fig, grafico = plt.subplots(figsize=(15, 5), dpi=100) #fig, grafico = plt.subplots(figsize=(15, 5), dpi=100) 
    #grafico.plot(x, y, lw=1, label='Potencia Aparente', marker='o')
    grafico.plot(x, y, lw=3, label='Pot. Historica', marker='o', color='k')
    grafico.plot(x, pred, lw=3, label='Tend. Lineal', marker='o', linestyle='dotted', color='b')
    grafico.plot(x, p_inst, lw=2, label='Pot.Instalada', color='r')
    grafico.plot(x, precau, lw=2, label='Precaucion N-1', color='y')
    
    grafico.set_ylim ([20,90])
    
    grafico.set_xlabel('[ Años ]')  # Add an x-label to the axes.
    grafico.set_ylabel('[ MVA ]')  # Add a y-label to the axes.
    grafico.set_title("ET San Francisco: Demanda Historica y Pronostico al 2026 ")  # Add a title to the axes.
    grafico.legend(loc='lower right')  # Add a legend.
    #grafico.annotate('Pico Max', xy=(01-31 14:45, 75), xytext=(01-31 15:00, 75), arrowprops=dict(facecolor='black', shrink=0.05)
    #grafico.grid() 
    
    #fig.savefig("Serie_Anual_15min_"+anio+".png")

Grafica_Hist_Pred()

<Figure size 640x480 with 0 Axes>

# Grafica anual de la Potencia Aparente cada 15 minutos
# (Trafos + Generacion)

def Grafica_Hist_Pred2():
    plt.style.use('fivethirtyeight')
    
    #y = Tablaxls.loc['2010':'2021', 'S_MVA']
    #x_y = Tablaxls.loc['2010':'2021', 'ANIO']
    
    y = Tablaxls.loc[:, 'S_MVA']
    pred = Tablaxls.loc[:, 'S_MVA_PRED']
    p_inst = Tablaxls.loc[:, 'POT_INSTALADA']
    precau = Tablaxls.loc[:, 'N-1']
    
    x = Tablaxls.loc[:, 'ANIO']
    
    #fig = plt.figure()
    #fig, grafico = plt.subplots(figsize=(15, 5), dpi=100)
    fig, grafico = plt.subplots(figsize=(7, 4))

    
    #grafico.plot(x, y, lw=1, label='Potencia Aparente', marker='o')
    grafico.plot(x, y, lw=2, label='Pot. Historica', marker='o', color='k')
    grafico.plot(x, pred, lw=2, label='Tend. Lineal', marker='o', linestyle='dotted', color='b')
    #grafico.plot(x, p_inst, lw=2, label='Pot.Instalada', color='r')
    grafico.plot(x, precau, lw=2, label='Precaucion N-1', color='y')
    
    #grafico.set_ylim ([20,90])
    
    ax.minorticks_on()
    ax.grid(b=True, which='major', color='blue', alpha=1, linewidth=1)
    ax.grid(b=True, which='minor', color='beige', alpha=1, linewidth=1)
    # Major Tick Label
    # Minor Tick Label
    #plt.xticks(fontsize=10)
    #x_rotulos=[2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026]
    x_rotulos = np.arange(2010,2027,1)
    plt.xticks(ticks=x, labels=x_rotulos, fontsize='x-small') 
    plt.yticks(fontsize=10)
    
    
    grafico.set_xlabel('[ Años ]', fontsize=10)  # Add an x-label to the axes.
    grafico.set_ylabel('[ MVA ]', fontsize=10)  # Add a y-label to the axes.
    grafico.set_title("ET San Francisco: Demanda Historica y Pronostico al 2026 ", fontsize=12)  # Add a title to the axes.
    grafico.legend(loc='lower right', fontsize=10)  # Add a legend.
    #grafico.annotate('Pico Max', xy=(01-31 14:45, 75), xytext=(01-31 15:00, 75), arrowprops=dict(facecolor='black', shrink=0.05)
    #grafico.grid() 
    
    #fig.savefig("Serie_Anual_15min_"+anio+".png")

Grafica_Hist_Pred2()

# Grafica anual de la Potencia Aparente cada 15 minutos
# (Trafos + Generacion)

def Grafica_Hist_Pred_Seaborn():
    plt.style.use('fivethirtyeight')
    
    #y = Tablaxls.loc['2010':'2021', 'S_MVA']
    #x_y = Tablaxls.loc['2010':'2021', 'ANIO']
    
    y = Tablaxls.loc[:, 'S_MVA']
    pred = Tablaxls.loc[:, 'S_MVA_PRED']
    p_inst = Tablaxls.loc[:, 'POT_INSTALADA']
    precau = Tablaxls.loc[:, 'N-1']
    
    x = Tablaxls.loc[:, 'ANIO']
    
    #fig = plt.figure()
    #fig, grafico = plt.subplots(figsize=(15, 5), dpi=100)
    fig, grafico = plt.subplots(figsize=(7, 4))
    
    sns.lineplot(x=x, y=y, err_style="bars", ci=68)

Grafica_Hist_Pred_Seaborn()

Tablaxls.info()

<class 'pandas.core.frame.DataFrame'>
Index: 17 entries, 2010 to 2026
Data columns (total 10 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   ANIO           17 non-null     int64  
 1   FECHA_HORA     12 non-null     object 
 2   P_MW           12 non-null     float64
 3   Q_MVAR         12 non-null     float64
 4   S_MVA          12 non-null     float64
 5   CRECIMIENTO_%  11 non-null     float64
 6   S_MVA_PRED     17 non-null     float64
 7   POT_INSTALADA  17 non-null     float64
 8   N-1            17 non-null     float64
 9   CARGA_%        17 non-null     float64
dtypes: float64(8), int64(1), object(1)
memory usage: 2.0+ KB

Tablaxls["RSE"] = (Tablaxls["S_MVA"]-Tablaxls["S_MVA_PRED"])**2
Tablaxls

	ANIO	FECHA_HORA	P_MW	Q_MVAR	S_MVA	CRECIMIENTO_%	S_MVA_PRED	POT_INSTALADA	N-1	CARGA_%	RSE
2010	2010	2010-01-05 14:00:00	36.24	8.89	37.32	NaN	40.82	75.00	50.00	49.76	12.22
2011	2011	2011-12-20 13:30:00	39.13	9.23	40.20	7.72	42.11	75.00	50.00	53.60	3.64
2012	2012	2012-02-16 14:00:00	42.13	9.47	43.18	7.41	43.40	75.00	50.00	57.57	0.05
2013	2013	2013-12-26 15:00:00	41.97	7.88	42.70	-1.11	44.69	75.00	50.00	56.93	3.98
2014	2014	2014-01-23 14:00:00	48.67	10.21	49.73	16.46	45.99	75.00	50.00	66.31	14.01
2015	2015	2015-03-11 14:00:00	44.64	12.43	46.34	-6.82	47.28	75.00	50.00	61.79	0.88
2016	2016	2016-02-12 13:15:00	45.93	8.94	46.79	0.97	48.57	75.00	50.00	62.39	3.18
2017	2017	2017-01-27 09:30:00	2.95	42.47	75.78	61.96	49.87	75.00	50.00	101.04	671.54
2018	2018	2018-02-07 13:45:00	46.22	3.40	46.35	-38.84	51.16	75.00	50.00	61.80	23.12
2019	2019	2019-02-22 14:15:00	45.63	3.57	45.77	-1.25	52.45	75.00	50.00	61.03	44.64
2020	2020	2020-12-28 13:15:00	41.71	1.59	41.74	-8.80	53.74	75.00	50.00	55.65	144.10
2021	2021	2021-05-04 11:30:00	26.85	52.78	59.22	41.88	55.04	75.00	50.00	78.96	17.50
2022	2022	NaN	NaN	NaN	NaN	NaN	47.74	75.00	50.00	63.66	NaN
2023	2023	NaN	NaN	NaN	NaN	NaN	48.26	75.00	50.00	64.34	NaN
2024	2024	NaN	NaN	NaN	NaN	NaN	48.77	75.00	50.00	65.03	NaN
2025	2025	NaN	NaN	NaN	NaN	NaN	49.28	75.00	50.00	65.71	NaN
2026	2026	NaN	NaN	NaN	NaN	NaN	49.80	75.00	50.00	66.40	NaN

plt.hist((Tablaxls["S_MVA"]-Tablaxls["S_MVA_PRED"]))

(array([1., 2., 5., 1., 2., 0., 0., 0., 0., 1.]),
 array([-12.00425408,  -8.21241492,  -4.42057576,  -0.6287366 ,
          3.16310256,   6.95494172,  10.74678089,  14.53862005,
         18.33045921,  22.12229837,  25.91413753]),
 <BarContainer object of 10 artists>)

Tablaxls.info()

<class 'pandas.core.frame.DataFrame'>
Index: 17 entries, 2010 to 2026
Data columns (total 11 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   ANIO           17 non-null     int64  
 1   FECHA_HORA     12 non-null     object 
 2   P_MW           12 non-null     float64
 3   Q_MVAR         12 non-null     float64
 4   S_MVA          12 non-null     float64
 5   CRECIMIENTO_%  11 non-null     float64
 6   S_MVA_PRED     17 non-null     float64
 7   POT_INSTALADA  17 non-null     float64
 8   N-1            17 non-null     float64
 9   CARGA_%        17 non-null     float64
 10  RSE            12 non-null     float64
dtypes: float64(9), int64(1), object(1)
memory usage: 1.6+ KB

Conclusiones